We take the Residual Network (ResNet) [1]} class of convolutional neural networks that are initially pre-trained on the ImageNet dataset [2]} comprised of multi-faceted set of 14 million images. Multiple copies of these ResNet models are then taken and joined together via a decision tree, such that each ResNet model is best responsive to corresponding disaster types that we achieve through fine-tuning. We use multiple ResNet sized models with corresponding loss functions such as cross-entropy, and ordinal-cross-entropy during fine-tuning. Our setup comprised of RTX 2080-Ti (12 GiB) and v3-8 TPU-node (8 TPU cores, 128 GiB). As our experiments evolved across ResNet-18, ResNet-34 and ResNet-50, epoch times increased significantly (e.g., ResNet-34 took 76 minutes per epoch that equates to 25.33 hours for 20 epochs). Thus, we perform experiments with the first two ResNets using Adam optimizer with learning-rate 0.001, gamma of 0.1, learning-rate-decay step-size of 7, and trained over 20 epochs.
